Search results for "NEURAL-NETWORKS"
showing 4 items of 4 documents
Retrieving infinite numbers of patterns in a spin-glass model of immune networks
2013
The similarity between neural and immune networks has been known for decades, but so far we did not understand the mechanism that allows the immune system, unlike associative neural networks, to recall and execute a large number of memorized defense strategies {\em in parallel}. The explanation turns out to lie in the network topology. Neurons interact typically with a large number of other neurons, whereas interactions among lymphocytes in immune networks are very specific, and described by graphs with finite connectivity. In this paper we use replica techniques to solve a statistical mechanical immune network model with `coordinator branches' (T-cells) and `effector branches' (B-cells), a…
Volatile Organic Compounds (VOCs) for Noninvasive Plant Diagnostics
2013
Auditory and Cognitive Deficits Associated with Acquired Amusia after Stroke: A Magnetoencephalography and Neuropsychological Follow-Up Study
2010
Acquired amusia is a common disorder after damage to the middle cerebral artery (MCA) territory. However, its neurocognitive mechanisms, especially the relative contribution of perceptual and cognitive factors, are still unclear. We studied cognitive and auditory processing in the amusic brain by performing neuropsychological testing as well as magnetoencephalography (MEG) measurements of frequency and duration discrimination using magnetic mismatch negativity (MMNm) recordings. Fifty-three patients with a left (n = 24) or right (n = 29) hemisphere MCA stroke (MRI verified) were investigated 1 week, 3 months, and 6 months after the stroke. Amusia was evaluated using the Montreal Battery of …
Fast Training of Self Organizing Maps for the Visual Exploration of Molecular Compounds
2007
Visual exploration of scientific data in life science\ud area is a growing research field due to the large amount of\ud available data. The Kohonen’s Self Organizing Map (SOM) is\ud a widely used tool for visualization of multidimensional data.\ud In this paper we present a fast learning algorithm for SOMs\ud that uses a simulated annealing method to adapt the learning\ud parameters. The algorithm has been adopted in a data analysis\ud framework for the generation of similarity maps. Such maps\ud provide an effective tool for the visual exploration of large and\ud multi-dimensional input spaces. The approach has been applied\ud to data generated during the High Throughput Screening\ud of mo…